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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-Assisted Security | 24% | - Operational Use of AI
|
| Topic 2: Securing AI Systems | 40% | - AI System Protection
|
| Topic 3: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
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NEW QUESTION # 13
An organization is developing and implementing AI features into a customer service application.
Which of the following practices should the organization put the place before releasing the application for customer trials?
Answer: C
Explanation:
Before releasing AI features for customer trials, it is critical to protect sensitive information that may be used during testing. Data masking and sanitization ensure customer or corporate data is anonymized or obfuscated, reducing the risk of data exposure while still allowing realistic evaluation of the AI system.
NEW QUESTION # 14
User experience is declining since the launch of a large language model (LLM) in internal networks. Which of the following should be the highest priority for the prompt engineers?
Answer: C
Explanation:
When user experience is declining after an LLM launch, the top priority for prompt engineers is quality control. Ensuring prompts produce accurate, relevant, and safe outputs directly improves usability and restores user trust.
NEW QUESTION # 15
Which of the following is the primary purpose of validating data for an AI system?
Answer: C
Explanation:
Basic Concept: Data validation is a critical step in the AI development pipeline that involves verifying that the data used for training and inference meets quality standards, is representative, and is free from systematic errors that could introduce bias. The quality of training data directly determines the quality and fairness of model outputs. CompTIA SecAI+ Study Guide covers data validation under AI development and responsible AI principles.
Why D is Correct: The primary purpose of data validation for an AI system is to ensure that the data is accurate, representative, and free from systematic errors that would cause the model to produce biased or discriminatory outcomes. Validating data checks for class imbalance, demographic underrepresentation, labeling errors, and corrupted values that could embed biases into the model. This ensures the AI system produces fair, accurate, and trustworthy outputs across all user groups.
Why A is Wrong: Automating the process is a benefit of using automated data validation tools but is not the primary purpose of validation itself. The automation serves the validation goal rather than being the reason validation is performed.
Why B is Wrong: Reducing resource consumption is an engineering optimization concern. Data validation may reduce resource waste by preventing training on poor-quality data, but this is a secondary benefit, not the primary purpose.
Why C is Wrong: Optimizing storage databases is a database engineering concern about performance and efficiency. Data validation examines data quality and representativeness for AI purposes, not database architecture optimization.
NEW QUESTION # 16
Which are indicators of lateral movement? (Choose two.)
Answer: A,D
Explanation:
Lateral movement often involves internal SMB attacks and credential reuse.
NEW QUESTION # 17
A security alert triggers an agentic system. An analyst notices the following payload in the logs"
The alert includes multiple shell commands that are not typically run as part of any hardening.
Which of the following is the most effective control to implement?
Answer: D
Explanation:
The payload in the alert attempts to trick the system into executing unauthorized shell commands.
The most effective control is to implement allow-list validation (approved strings) before execution. This ensures that only predefined, safe commands are executed, blocking prompt injection attempts that introduce malicious code such as the fake patch script.
NEW QUESTION # 18
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